Reconstruct the directory structure

config/baseline/baseline.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: CASIA-B
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/CASIA-B_include_005.json
+  dataset_partition: ./datasets/CASIA-B/CASIA-B_include_005.json
   num_workers: 1
   remove_no_gallery: false # Remove probe if no gallery for it
   test_dataset_name: CASIA-B

config/baseline/baseline_GREW.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: GREW
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/GREW.json
+  dataset_partition: ./datasets/GREW/GREW.json
   num_workers: 16
   remove_no_gallery: false # Remove probe if no gallery for it
   test_dataset_name: GREW

config/baseline/baseline_OUMVLP.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: OUMVLP
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/OUMVLP.json
+  dataset_partition: ./datasets/OUMVLP/OUMVLP.json
   num_workers: 1
   remove_no_gallery: false # Remove probe if no gallery for it
   test_dataset_name: OUMVLP

config/baseline/baseline_hid.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: HID
   dataset_root: your_path
-  dataset_partition: ./misc/HID/HID.json
+  dataset_partition: ./datasets/HID/HID.json
   num_workers: 1
   remove_no_gallery: false # Remove probe if no gallery for it
 evaluator_cfg:

config/default.yaml

@@ -2,7 +2,7 @@ data_cfg:
   dataset_name: CASIA-B
   dataset_root: your_path
   num_workers: 1
-  dataset_partition: ./misc/partitions/CASIA-B.json
+  dataset_partition: ./datasets/CASIA-B/CASIA-B.json
   remove_no_gallery: false
   cache: false
   test_dataset_name: CASIA-B

config/gaitgl/gaitgl.yaml

@@ -2,7 +2,7 @@
 data_cfg:
   dataset_name: CASIA-B
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/CASIA-B_include_005.json
+  dataset_partition: ./datasets/CASIA-B/CASIA-B_include_005.json
   num_workers: 1
   remove_no_gallery: false
   test_dataset_name: CASIA-B

config/gaitgl/gaitgl_OUMVLP.yaml

@@ -2,7 +2,7 @@
 data_cfg:
   dataset_name: OUMVLP
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/OUMVLP.json
+  dataset_partition: ./datasets/OUMVLP/OUMVLP.json
   num_workers: 1
   remove_no_gallery: false
   test_dataset_name: OUMVLP

config/gaitpart/gaitpart_OUMVLP.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: OUMVLP
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/OUMVLP.json
+  dataset_partition: ./datasets/OUMVLP/OUMVLP.json
   num_workers: 4
   remove_no_gallery: false
   test_dataset_name: OUMVLP

config/gaitset/gaitset_OUMVLP.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: OUMVLP
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/OUMVLP.json
+  dataset_partition: ./datasets/OUMVLP/OUMVLP.json
   num_workers: 4
   remove_no_gallery: false
   test_dataset_name: OUMVLP

config/gln/gln_phase1.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: CASIA-B
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/CASIA-B_include_005.json
+  dataset_partition: ./datasets/CASIA-B/CASIA-B_include_005.json
   num_workers: 1
   cache: false
   remove_no_gallery: false

config/gln/gln_phase2.yaml

@@ -1,7 +1,7 @@
 data_cfg:
   dataset_name: CASIA-B
   dataset_root: your_path
-  dataset_partition: ./misc/partitions/CASIA-B_include_005.json
+  dataset_partition: ./datasets/CASIA-B/CASIA-B_include_005.json
   num_workers: 1
   remove_no_gallery: false
   test_dataset_name: CASIA-B

datasets/CASIA-B/README.md

@@ -0,0 +1,27 @@
+# CASIA-B
+Download URL: http://www.cbsr.ia.ac.cn/GaitDatasetB-silh.zip
+- Original
+    ```
+    CASIA-B
+        001 (subject)
+            bg-01 (type)
+                000 (view)
+                    001-bg-01-000-001.png (frame)
+                    001-bg-01-000-002.png (frame)
+                    ......
+                ......
+            ......
+        ......
+    ```
+- Run `python misc/pretreatment.py --input_path CASIA-B --output_path CASIA-B-pkl`
+- Processed
+    ```
+    CASIA-B-pkl
+        001 (subject)
+            bg-01 (type)
+                    000 (view)
+                        000.pkl (contains all frames)
+                ......
+            ......
+        ......
+    ```

datasets/GREW/README.md

@@ -26,12 +26,12 @@ After unpacking these compressed files, run this command:
 
 Step2 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run 
 ```
-python misc/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
+python datasets/GREW/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
 ```  
 
 Step3: Transforming images to pickle file, run 
 ```
-python misc/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
+python datasets/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
 ```
 Then you will see the structure like:
 
@@ -62,17 +62,16 @@ Then you will see the structure like:
     ```
 
 ## Train the dataset
-Modify the `dataset_root` in `./config/baseline_GREW.yaml`, and then run this command:
+Modify the `dataset_root` in `./config/baseline/baseline_GREW.yaml`, and then run this command:
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline_GREW.yaml --phase train
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline/baseline_GREW.yaml --phase train
 ```
-<!-- You can also download the [trained model](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) and place it in `output` after unzipping. -->
 
 ## Get the submission file
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline_GREW.yaml --phase test
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/baseline/baseline_GREW.yaml --phase test
 ```
 The result will be generated in your working directory, you must rename and compress it as the requirements before submitting.
 
 ## Evaluation locally
-While the original grew treat both seq_01 and seq_02 as gallery, but there is no ground truth for probe. Therefore, it is nessesary to upload the submission file on grew competitation. We seperate test set to: seq_01 as gallery, seq_02 as probe. Then you can modify `eval_func` in the `./config/baseline_GREW.yaml` to `identification_real_scene`, you can obtain result localy like setting of OUMVLP. 
+While the original grew treat both seq_01 and seq_02 as gallery, but there is no ground truth for probe. Therefore, it is nessesary to upload the submission file on grew competitation. We seperate test set to: seq_01 as gallery, seq_02 as probe. Then you can modify `eval_func` in the `./config/baseline/baseline_GREW.yaml` to `identification_real_scene`, you can obtain result localy like setting of OUMVLP. 

datasets/OUMVLP/README.md

@@ -0,0 +1,67 @@
+# OUMVLP
+Step1: Download URL: http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html
+
+Step2: Unzip the dataset, you will get a structure directory like:
+```
+python datasets/OUMVLP/extractor.py --input_path Path_of_OUMVLP-base --output_path Path_of_OUMVLP-raw --password Given_Password
+```  
+
+- Original
+    ```
+    OUMVLP-raw
+        Silhouette_000-00 (view-sequence)
+            00001 (subject)
+                0001.png (frame)
+                0002.png (frame)
+                ......
+            00002
+                0001.png (frame)
+                0002.png (frame)
+                ......
+            ......
+        Silhouette_000-01
+            00001
+                0001.png (frame)
+                0002.png (frame)
+                ......
+            00002
+                0001.png (frame)
+                0002.png (frame)
+                ......
+            ......
+        Silhouette_015-00
+            ......
+        Silhouette_015-01
+            ......
+        ......
+    ```
+Step3 : To rearrange directory of OUMVLP dataset, turning to id-type-view structure, Run 
+```
+python datasets/OUMVLP/rearrange_OUMVLP.py --input_path Path_of_OUMVLP-raw --output_path Path_of_OUMVLP-rearranged
+```  
+
+Step4: Transforming images to pickle file, run 
+```
+python datasets/pretreatment.py --input_path Path_of_OUMVLP-rearranged --output_path Path_of_OUMVLP-pkl
+```
+
+- Processed
+    ```
+    OUMVLP-pkl
+        00001 (subject)
+            00 (sequence)
+                000 (view)
+                    000.pkl (contains all frames)
+                015 (view)
+                    015.pkl (contains all frames)
+                ...
+            01 (sequence)
+                000 (view)
+                    000.pkl (contains all frames)
+                015 (view)
+                    015.pkl (contains all frames)
+                ......
+        00002 (subject)
+            ......
+        ......
+    ```

datasets/README.md

@@ -0,0 +1,24 @@
+# Datasets
+OpenGait officially supports a few gait datasets. In order to use them, you need to download them and use the code provided here to pre-process them to the format required by OpenGait.
+
+## Pre-process
+In general, we read the original image provided by the dataset and save a sequence as a pickle file to speed up the training IO. 
+
+The expected dataset structure is as follows:
+```
+    DATASET_ROOT/
+        001 (subject)/
+            bg-01 (type)/
+                    000 (view)/
+                        000.pkl (contains all frames)
+                ......
+            ......
+        ......
+```
+
+The specific preprocessing steps are described inside each dataset folder.
+
+## Split dataset
+For each dataset, we split the dataset into training and testing sets. The training set is used to train the model, and the testing set is used to evaluate the model.
+
+You can use the partition file in [dataset folder](CASIA-B/CASIA-B.json) directly, or you can create yours. Remember to set your path to the partition file in [config/*.yaml](../config/).

docs/0.prepare_dataset.md

@@ -18,7 +18,7 @@ Download URL: http://www.cbsr.ia.ac.cn/GaitDatasetB-silh.zip
             ......
         ......
     ```
-- Run `python misc/pretreatment.py --input_path CASIA-B --output_path CASIA-B-pkl`
+- Run `python datasets/pretreatment.py --input_path CASIA-B --output_path CASIA-B-pkl`
 - Processed
     ```
     CASIA-B-pkl
@@ -36,7 +36,7 @@ Step1: Download URL: http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.htm
 
 Step2: Unzip the dataset, you will get a structure directory like:
 ```
-python misc/extractor.py --input_path Path_of_OUMVLP-base --output_path Path_of_OUMVLP-raw --password Given_Password
+python datasets/OUMVLP/extractor.py --input_path Path_of_OUMVLP-base --output_path Path_of_OUMVLP-raw --password Given_Password
 ```  
 
 - Original
@@ -70,12 +70,12 @@ python misc/extractor.py --input_path Path_of_OUMVLP-base --output_path Path_of_
     ```
 Step3 : To rearrange directory of OUMVLP dataset, turning to id-type-view structure, Run 
 ```
-python misc/rearrange_OUMVLP.py --input_path Path_of_OUMVLP-raw --output_path Path_of_OUMVLP-rearranged
+python datasets/OUMVLP/rearrange_OUMVLP.py --input_path Path_of_OUMVLP-raw --output_path Path_of_OUMVLP-rearranged
 ```  
 
 Step4: Transforming images to pickle file, run 
 ```
-python misc/pretreatment.py --input_path Path_of_OUMVLP-rearranged --output_path Path_of_OUMVLP-pkl
+python datasets/pretreatment.py --input_path Path_of_OUMVLP-rearranged --output_path Path_of_OUMVLP-pkl
 ```
 
 - Processed
@@ -135,12 +135,12 @@ Step2: [Unzip](https://github.com/GREW-Benchmark/GREW-Benchmark) the dataset, yo
             
 Step3 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run 
 ```
-python misc/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
+python datasets/GREW/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
 ```  
 
 Step4: Transforming images to pickle file, run 
 ```
-python misc/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
+python datasets/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl
 ```
 
 - Processed
@@ -170,4 +170,4 @@ python misc/pretreatment.py --input_path Path_of_GREW-rearranged --output_path P
     ```
 
 ## Split dataset
-You can use the partition file in [misc/partitions](misc/partitions/) directly, or you can create yours. Remember to set your path to the partition file in [config/*.yaml](config/).
+You can use the partition file in dataset folder directly, or you can create yours. Remember to set your path to the partition file in [config/*.yaml](config/).

docs/3.advanced_usages.md

@@ -1,13 +1,13 @@
 # Advanced Usages
 ### Cross-Dataset Evalution
-> You can conduct cross-dataset evalution by just modifying several arguments in your [data_cfg](../config/baseline.yaml#L1).
+> You can conduct cross-dataset evalution by just modifying several arguments in your [data_cfg](../config/baseline/baseline.yaml#L1).
 >
->  Take [baseline.yaml](../config/baseline.yaml) as an example:
+>  Take [baseline.yaml](../config/baseline/baseline.yaml) as an example:
 > ```yaml
 > data_cfg:
 >   dataset_name: CASIA-B
 >   dataset_root:  your_path
->   dataset_partition: ./misc/partitions/CASIA-B_include_005.json
+>   dataset_partition: ./datasets/CASIA-B/CASIA-B_include_005.json
 >   num_workers: 1
 >   remove_no_gallery: false # Remove probe if no gallery for it
 >   test_dataset_name: CASIA-B
@@ -19,7 +19,7 @@
 > data_cfg:
 >   dataset_name: CASIA-B
 >   dataset_root:  your_OUMVLP_path
->   dataset_partition: ./misc/partitions/OUMVLP.json
+>   dataset_partition: ./datasets/OUMVLP/OUMVLP.json
 >   num_workers: 1
 >   remove_no_gallery: false # Remove probe if no gallery for it
 >   test_dataset_name: OUMVLP

test.sh

@@ -1,15 +1,15 @@
 # # **************** For CASIA-B ****************
 # # Baseline
-CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline.yaml --phase test
+CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline/baseline.yaml --phase test
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitset.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitset/gaitset.yaml --phase test
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitpart.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitpart/gaitpart.yaml --phase test
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --master_port 12345 --nproc_per_node=4 opengait/main.py --cfgs ./config/gaitgl.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --master_port 12345 --nproc_per_node=4 opengait/main.py --cfgs ./config/gaitgl/gaitgl.yaml --phase test
 
 # # GLN 
 # # Phase 1
@@ -20,13 +20,13 @@ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 o
 
 # # **************** For OUMVLP ****************
 # # Baseline
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/baseline_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/baseline/baseline_OUMVLP.yaml --phase test
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitset_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitset/gaitset_OUMVLP.yaml --phase test
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitpart_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitpart/gaitpart_OUMVLP.yaml --phase test
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitgl_OUMVLP.yaml --phase test
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitgl/gaitgl_OUMVLP.yaml --phase test

train.sh

@@ -1,15 +1,15 @@
 # # **************** For CASIA-B ****************
 # # Baseline
-CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline.yaml --phase train
+CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/baseline/baseline.yaml --phase train
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitset.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitset/gaitset.yaml --phase train
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitpart.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./config/gaitpart/gaitpart.yaml --phase train
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/gaitgl.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./config/gaitgl/gaitgl.yaml --phase train
 
 # # GLN 
 # # Phase 1
@@ -20,13 +20,13 @@ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 o
 
 # # **************** For OUMVLP ****************
 # # Baseline
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/baseline_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/baseline/baseline_OUMVLP.yaml --phase train
 
 # # GaitSet
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitset_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitset/gaitset_OUMVLP.yaml --phase train
 
 # # GaitPart
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitpart_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitpart/gaitpart_OUMVLP.yaml --phase train
 
 # GaitGL
-# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitgl_OUMVLP.yaml --phase train
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 opengait/main.py --cfgs ./config/gaitgl/gaitgl_OUMVLP.yaml --phase train